Fonseca anamnestic index for screening temporomandibular disorders - reliability to discriminate muscular from intra-articular disorders

• Autor(a) principal: São João, Ricardo
• Data de Publicação: 2022
• Outros Autores: Cardoso, Henrique José, Sanz, David, Ângelo, David Faustino
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.15/4242
• topic: Fonseca Anamnestic Index; Intra-articular temporomandibular Disorders; Masticatory muscle temporomandibular disorders; Multinomial logistic regression; Patient-reported questionnaire; Temporomandibular Disorders

Background/ Objective: Fonseca anamnestic index (FAI) is a simple and quick survey used for screening the presence and severity of Temporomandibular Disorders (TMD). The presented study aimed to screen the FAI accuracy to discriminate different types of TMD: intra-articular (AD), Masticatory Muscular Disorder (MMD), or the presence of both typologies. Methods: The existence of a pattern in the FAI based on the frequency of answers was evaluated and supported by other variables: sex, age, medical diagnosis and Visual Analog Scale of health-related quality of Life (VASLife). The non-parametric Chi-square test () or Fisher's exact test were used to assess the existence of associations between these variables. In the pairs of variables where such association was identified, its intensity was measured by Cramér's V Coefficient. The prediction if FAI could be a good decision tool for distinguish the type of TMD was assessed through logistic regression models (ordinal and multinomial). Results: The higher FAI score was associated with questions related with temporomandibular joint (TMJ) pain, TMJ clicks and person anxiety. Severe cases classified by FAI are correlated with typology of Both (AD+MMD). Moreover, the female patients presented more moderate and severe cases in FAI and also correlated with the presence of AD+MMD. The logistic model showed low accuracy to distinguish the TMD typology (~70%). Conclusion: FAI is a good initial methodology in TMD diagnosis, however integrated in a logistic regression model for distinguish the typology of TMD has proved to be insufficient. It is expected that the combination of this survey with other outcomes will make the model more accurate.